1,281 research outputs found

    Incorporating Personalization Features in a Hospital-Stay Summary Generation System

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    Most of the currently available health resources contain vast amount of information that are created by keeping the “general” population in mind, which in reality, might not be useful for anyone. One approach to providing comprehensible health information to patients is to generate summaries that are personalized to each individual. This paper details the design of a personalized hospital-stay summary generation system that tailors its content to the patient’s understanding of medical terminologies and their level of engagement in improving their own health. Our summaries were found to cover around 80% of the health concepts that were considered as important by a doctor or a nurse. An online survey conducted on 150 participants verified that our algorithm’s interpretation of the personalization parameters is representative of that of a larger population

    Patients' position in care transitions : an analysis of patient participation and patient-centeredness

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    Introduction: Patients with chronic diseases need care transitions between primary and hospital care when facing severe exacerbation or acute illness. Such transitions are associated with risks, potentially adverse events and patient suffering. To improve care transitions, patients’ and healthcare professionals’ experiences and perspectives of patient participation and patient-centeredness need to be explored. Aim: The general aim of this thesis is to improve the knowledge and understanding of patient participation and patient-centeredness in handovers between primary and hospital care. Methods: The thesis comprises four papers about patients with chronic diseases (asthma, diabetes mellitus I or II, chronic heart failure, chronic obstructive pulmonary disease) and/or polypharmacy, and the healthcare professionals who treated the patients in the hospital and the primary healthcare. The study was conducted in five European countries: the Netherlands, Spain, Poland, Italy and Sweden. Both qualitative (papers I, II, and III) and quantitative (paper IV) methodology are used. Paper I is a content analysis of individual interviews with 23 Swedish patients. Paper II is a secondary analysis of both individual and focus group data of 90 patients from the five countries. Paper III is a meta-synthesis of both individual and focus group data of 90 patients and 258 healthcare professionals from the five countries. Paper IV includes medical records of 22 Swedish patients by review and assessment of their handover records. Results: Patients participated through both verbal activities (information exchange) and non-verbal activities (e.g. transfer of medication lists, referrals, and discharge notes). Patients’ activity varied from taking responsibility for handover, via shared responsibility, to being passive. The patients’ capacity for participation was reduced by health condition and health illiteracy, and strengthened by personality, experience and social network. Patients felt empowered by the knowledge they received through participation. Patients and healthcare professionals experienced both patient-centered handovers (patient needs addressed and discussed; responsive relations in which personalized information was provided; having continuity of care) as well as non patient-centered handovers. Organizational factors such as lack of time; emergent needs of other patients; and shift work forced the healthcare professionals to discharge patients without needs properly assessed; in discharge encounters held in a rush or without encounters; and by healthcare professionals who had not treated the patient at the ward. Conclusions: Based on the findings, improved handovers – ensuring that information reaches the next setting – would mean having formal discharge encounters, and empowering patients with information, education and clarification of the handover process. In such cases, the patients can participate in handovers through exchange of information about their self-management, care and treatment in the present encounter, the next encounter and the handover between those. Organizational factors contribute to healthcare professionals’ patient-centeredness, and patient-centeredness seems to increase patients’ participation in handovers. The interactive aspects should be encouraged, an organization providing allocated time and recourses, and a following patient-centered attitude of the healthcare professionals could benefit all involved stakeholders resulting in patient-centered handovers with participating patients

    Practical Use of ChatGPT in Psychiatry for Treatment Plan and Psychoeducation

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    Artificial Intelligence (AI) has revolutionized various fields, including medicine and mental health support. One promising application is ChatGPT, an advanced conversational AI model that uses deep learning techniques to provide human-like responses. This review paper explores the potential impact of ChatGPT in psychiatry and its various applications, highlighting its role in therapy and counseling techniques, self-help and coping strategies, mindfulness and relaxation techniques, screening and monitoring, education and information dissemination, specialized support, group and family support, learning and training, expressive and artistic therapies, telepsychiatry and online support, and crisis management and prevention. While ChatGPT offers personalized, accessible, and scalable support, it is essential to emphasize that it should not replace the expertise and guidance of qualified mental health professionals. Ethical considerations, such as user privacy, data security, and human oversight, are also discussed. By examining the potential and challenges, this paper sheds light on the responsible integration of ChatGPT in psychiatric research and practice, fostering improved mental health outcomes

    Patient-Clinician Communications: A Design Intervention for Patients' Comprehension of Their Care

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    Master of Design in Integrative DesignUniversity of Michiganhttps://deepblue.lib.umich.edu/bitstream/2027.42/136866/1/JYShin_2017_MDes-Thesis.pd

    Clinical Text Summarization: Adapting Large Language Models Can Outperform Human Experts

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    Sifting through vast textual data and summarizing key information imposes a substantial burden on how clinicians allocate their time. Although large language models (LLMs) have shown immense promise in natural language processing (NLP) tasks, their efficacy across diverse clinical summarization tasks has not yet been rigorously examined. In this work, we employ domain adaptation methods on eight LLMs, spanning six datasets and four distinct summarization tasks: radiology reports, patient questions, progress notes, and doctor-patient dialogue. Our thorough quantitative assessment reveals trade-offs between models and adaptation methods in addition to instances where recent advances in LLMs may not lead to improved results. Further, in a clinical reader study with six physicians, we depict that summaries from the best adapted LLM are preferable to human summaries in terms of completeness and correctness. Our ensuing qualitative analysis delineates mutual challenges faced by both LLMs and human experts. Lastly, we correlate traditional quantitative NLP metrics with reader study scores to enhance our understanding of how these metrics align with physician preferences. Our research marks the first evidence of LLMs outperforming human experts in clinical text summarization across multiple tasks. This implies that integrating LLMs into clinical workflows could alleviate documentation burden, empowering clinicians to focus more on personalized patient care and other irreplaceable human aspects of medicine.Comment: 23 pages, 22 figure

    Developing a Tool to Support Decisions on Patient Prioritization at Admission to Home Health Care

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    Background and aims: Millions of Americans are discharged from hospitals to home health every year and about third of them return to hospitals. A significant number of rehospitalizations (up to 60%) happen within the first two weeks of services. Early targeted allocation of services for patients who need them the most, have the potential to decrease readmissions. Unfortunately, there is only fragmented evidence on factors that should be used to identify high-risk patients in home health. This dissertation study aimed to (1) identify factors associated with priority for the first home health nursing visit and (2) to construct and validate a decision support tool for patient prioritization. I recruited a geographically diverse convenience sample of nurses with expertise in care transitions and care coordination to identify factors supporting home health care prioritization. Methods: This was a predictive study of home health visit priority decisions made by 20 nurses for 519 older adults referred to home health. Variables included sociodemographics, diagnosis, comorbid conditions, adverse events, medications, hospitalization in last 6 months, length of stay, learning ability, self-rated health, depression, functional status, living arrangement, caregiver availability and ability and first home health visit priority decision. A combination of data mining and logistic regression models was used to construct and validate the final model. Results: The final model identified five factors associated with first home health visit priority. A cutpoint for decisions on low/medium versus high priority was derived with a sensitivity of 80% and specificity of 57.9%, area under receiver operator curve (ROC) 75.9%. Nurses were more likely to prioritize patients who had wounds (odds ratio [OR]=1.88), comorbid condition of depression (OR=1.73), limitation in current toileting status (OR= 2.02), higher numbers of medications (increase in OR for each medication =1.04) and comorbid conditions (increase in OR for each condition =1.04). Discussion: This dissertation study developed one of the first clinical decision support tools for home health, the PREVENT - Priority for Home Health Visit Tool. Further work is needed to increase the specificity and generalizability of the tool and to test its effects on patient outcomes

    Clinical text data in machine learning: Systematic review

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    Background: Clinical narratives represent the main form of communication within healthcare providing a personalized account of patient history and assessments, offering rich information for clinical decision making. Natural language processing (NLP) has repeatedly demonstrated its feasibility to unlock evidence buried in clinical narratives. Machine learning can facilitate rapid development of NLP tools by leveraging large amounts of text data. Objective: The main aim of this study is to provide systematic evidence on the properties of text data used to train machine learning approaches to clinical NLP. We also investigate the types of NLP tasks that have been supported by machine learning and how they can be applied in clinical practice. Methods: Our methodology was based on the guidelines for performing systematic reviews. In August 2018, we used PubMed, a multi-faceted interface, to perform a literature search against MEDLINE. We identified a total of 110 relevant studies and extracted information about the text data used to support machine learning, the NLP tasks supported and their clinical applications. The data properties considered included their size, provenance, collection methods, annotation and any relevant statistics. Results: The vast majority of datasets used to train machine learning models included only hundreds or thousands of documents. Only 10 studies used tens of thousands of documents with a handful of studies utilizing more. Relatively small datasets were utilized for training even when much larger datasets were available. The main reason for such poor data utilization is the annotation bottleneck faced by supervised machine learning algorithms. Active learning was explored to iteratively sample a subset of data for manual annotation as a strategy for minimizing the annotation effort while maximizing predictive performance of the model. Supervised learning was successfully used where clinical codes integrated with free text notes into electronic health records were utilized as class labels. Similarly, distant supervision was used to utilize an existing knowledge base to automatically annotate raw text. Where manual annotation was unavoidable, crowdsourcing was explored, but it remains unsuitable due to sensitive nature of data considered. Beside the small volume, training data were typically sourced from a small number of institutions, thus offering no hard evidence about the transferability of machine learning models. The vast majority of studies focused on the task of text classification. Most commonly, the classification results were used to support phenotyping, prognosis, care improvement, resource management and surveillance. Conclusions: We identified the data annotation bottleneck as one of the key obstacles to machine learning approaches in clinical NLP. Active learning and distant supervision were explored as a way of saving the annotation efforts. Future research in this field would benefit from alternatives such as data augmentation and transfer learning, or unsupervised learning, which does not require data annotation

    Complex Care Management Program Overview

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    This report includes brief updates on various forms of complex care management including: Aetna - Medicare Advantage Embedded Case Management ProgramBrigham and Women's Hospital - Care Management ProgramIndependent Health - Care PartnersIntermountain Healthcare and Oregon Health and Science University - Care Management PlusJohns Hopkins University - Hospital at HomeMount Sinai Medical Center -- New York - Mount Sinai Visiting Doctors Program/ Chelsea-Village House Calls ProgramsPartners in Care Foundation - HomeMeds ProgramPrinceton HealthCare System - Partnerships for PIECEQuality Improvement for Complex Chronic Conditions - CarePartner ProgramSenior Services - Project Enhance/EnhanceWellnessSenior Whole Health - Complex Care Management ProgramSumma Health/Ohio Department of Aging - PASSPORT Medicaid Waiver ProgramSutter Health - Sutter Care Coordination ProgramUniversity of Washington School of Medicine - TEAMcar

    A Novel Hybrid Based Method in Covid 19 Health System for Data Extraction with Blockchain Technology

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    Millions of people have been afflicted by the COVID-19 epidemic, which has resulted in hundreds of thousands of fatalities throughout the world. Extracting correct data on patients and facilities with and without COVID-19 with high confidence for medical specialists or the government is extremely difficult. As a result, utilizing blockchain technology, a reliable data extraction methodology for the COVID-19 database is constructed. In this accurate data extraction model development and validation study in blockchain technology for COVID analysis, here a novel Hybrid Deep Belief Lionized Optimization (HDBLO) approach is proposed. The weights of the deep model are optimized by the fitness of lion optimization. The implementation of this work is executed using MATLAB software. The simulation outcomes shows the effective performance of proposed model in blockchain technology in COVID paradigm in terms of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), accuracy, F-measure, Processing time, precision and error. Consequently, the proposed approach is compared with the conventional strategies for significant validation
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